Chien‐Wei Huang

Various attacks have emerged as the major threats to the success of a connected world like the Internet of Things (IoT), in which billions of devices interact with each other to facilitate human life. By exploiting the vulnerabilities of cheap and insecure devices such as IP cameras, an attacker can create hundreds of thousands of zombie devices and then launch massive volume attacks to take down any target. For example, in 2016, a record large-scale DDoS attack launched by millions of Mirai-injected IP cameras and smart printers blocked the accessibility of several high-profile websites. To date, the state-of-the-art defense systems against such attacks rely mostly on pre-defined features extracted from the entire flows or signatures. The feature definitions are manual, and it would be too late to block a malicious flow after extracting the flow features. In this work, we present an effective anomaly traffic detection mechanism, namely D-PACK, which consists of a Convolutional Neural Network (CNN) and an unsupervised deep learning model (e.g., Autoencoder) for auto-profiling the traffic patterns and filtering abnormal traffic. Notably, D-PACK inspects only the first few bytes of the first few packets in each flow for early detection. Our experimental results show that, by examining just the first two packets in each flow, D-PACK still performs with nearly 100% accuracy, while features an extremely low false-positive rate, e.g., 0.83%. The design can inspire the emerging efforts towards online anomaly detection systems that feature reducing the volume of processed packets and blocking malicious flows in time.

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.

Background: Heavy metals are known to be harmful for neurodevelopment and they may correlate to attention deficit/hyperactivity disorder (ADHD). In this study, we aim to explore the relationships between multiple heavy metals (manganese, lead, cadmium, mercury, antimony, and bismuth), neurocognitive function, and ADHD symptoms. Methods: We recruited 29 patients with ADHD inattentive type (ADHD-I), 47 patients with ADHD hyperactivity/impulsivity type (ADHD-H/I), and 46 healthy control children. Urine samples were obtained to measure the levels of the aforementioned heavy metals in each child. Participants’ cognitive function and clinical symptoms were assessed, respectively. Results: We found ADHD-H/I patients demonstrated the highest antimony levels (p = 0.028), and ADHD-I patients demonstrated the highest cadmium levels (p = 0.034). Antimony levels were positively correlated with the severity of ADHD symptoms that were rated by teachers, and cadmium levels were negatively correlated with the Full Scale Intelligence Quotient. Lead levels were negatively correlated with most indices of the Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV), but positively correlated with inattention and hyperactivity/impulsivity symptoms (p < 0.05). Conclusion: Lead, cadmium and antimony were associated with susceptibility to ADHD and symptom severity in school-age children. Eliminating exposure to heavy metals may help to prevent neurodevelopmental disorders in children.

In this study, the combination of hyperspectral imaging (HSI) technology and band selection was coupled with color reproduction. The white-light images (WLIs) were simulated as narrow-band endoscopic images (NBIs). As a result, the blood vessel features in the endoscopic image became more noticeable, and the prediction performance was improved. In addition, a single-shot multi-box detector model for predicting the stage and location of esophageal cancer was developed to evaluate the results. A total of 1780 esophageal cancer images, including 845 WLIs and 935 NBIs, were used in this study. The images were divided into three stages based on the pathological features of esophageal cancer: normal, dysplasia, and squamous cell carcinoma. The results showed that the mean average precision (mAP) reached 80% in WLIs, 85% in NBIs, and 84% in HSI images. This study's results showed that HSI has more spectral features than white-light imagery, and it improves accuracy by about 5% and matches the results of NBI predictions.

The rotten-egg odour of hydrogen sulfide (H2S) produced by the yeast Saccharomyces cerevisiae has attracted considerable research interest due to its huge impact on the sensory quality of fermented foods and beverages. To date, the yeast genetic mechanisms of H2S liberation during wine fermentation are well understood and yeast strains producing low levels of H2S have been developed. Studies have also revealed that H2S is not just a by-product in the biosynthesis of the sulfur-containing amino acids, but indeed a vital molecule involved in detoxification, population signalling and extending cellular life span. Moreover, polysulfides have recently emerged as key players in signalling and the sensory quality of wine because their degradation leads to the release of H2S. This review will focus on the recent findings on the production of H2S and polysulfides in S. cerevisiae and summarise their potential roles in yeast survival and winemaking. Recent advances in techniques for the detection of H2S and polysulfides offer an exciting opportunity to uncover the novel genes and pathways involved in their formation from different sulfur sources. This knowledge will not only provide further insights into yeast sulfur metabolism, but could potentially improve the sensory quality of wine.